OpenRaiser/Intern-Atlas huggingface dataset:
QUAD: A Quality Aware Multi-Unit Double Auction Framework for IoT-Based Mobile Crowdsensing in Strategic Setting
Venue: arXiv cs.GT 2022
Year: 2022

Crowdsourcing with the intelligent agents carrying smart devices is becoming increasingly popular in recent years. It has opened up meeting an extensive list of real life applications such as measuring air pollution level, road traffic information, and so on. In literature this is known as mobile crowdsourcing or mobile crowdsensing. In this paper, the discussed set-up consists of multiple task requesters (or task providers) and multiple IoT devices (as task executors), where each of the task providers is having multiple homogeneous sensing tasks. Each of the task requesters report bid along with the number of homogeneous sensing tasks to the platform. On the other side, we have multiple IoT devices that reports the ask (charge for imparting its services) and the number of sensing tasks that it can execute. The valuations of task requesters and IoT devices are private information, and both might act strategically. One assumption that is made in this paper is that the bids and asks of the agents (task providers and IoT devices) follow decreasing marginal returns criteria. In this paper, a truthful mechanism is proposed for allocating the IoT devices to the sensing tasks carried by task requesters, that also keeps into account the quality of IoT devices. The mechanism is truthful, budget balance, individual rational, computationally efficient, and prior-free. The simulations are carried out to measure the performance of the proposed mechanism against the benchmark mechanisms. The code and the synthetic data are available at \textbf{https://github.com/Samhitha-Jasti/QUAD-Implementation}.
Towards Personalized Intelligence at Scale
Venue: arXiv cs.CL 2022
Year: 2022

Personalized Intelligence (PI) is the problem of providing customized AI experiences tailored to each individual user. In many applications, PI is preferred or even required. Existing personalization approaches involve fine-tuning pre-trained models to create new customized models. However, these approaches require a significant amount of computation to train, scaling with model size and the number of users, inhibiting PI to be realized widely. In this work, we introduce a novel model architecture and training/inference framework to enable Personalized Intelligence at scale. We achieve this by attaching a Personalization Head (PH) to pre-trained language models (LM). During training, the base LMs are frozen and only the parameters in PH are updated and are unique per user. This results in significantly smaller overall model sizes and training cost than traditional fine-tuning approaches when scaled across many users. We evaluate PHs on academia and industry-focused datasets and show that the PHs outperform zeroshot baseline in F1 score and are significantly more scalable than traditional fine-tuning approaches. We identify key factors required for effective PH design and training.
Set-valued prediction in hierarchical classification with constrained representation complexity
Venue: arXiv cs.LG 2022
Year: 2022

Set-valued prediction is a well-known concept in multi-class classification. When a classifier is uncertain about the class label for a test instance, it can predict a set of classes instead of a single class. In this paper, we focus on hierarchical multi-class classification problems, where valid sets (typically) correspond to internal nodes of the hierarchy. We argue that this is a very strong restriction, and we propose a relaxation by introducing the notion of representation complexity for a predicted set. In combination with probabilistic classifiers, this leads to a challenging inference problem for which specific combinatorial optimization algorithms are needed. We propose three methods and evaluate them on benchmark datasets: a naïve approach that is based on matrix-vector multiplication, a reformulation as a knapsack problem with conflict graph, and a recursive tree search method. Experimental results demonstrate that the last method is computationally more efficient than the other two approaches, due to a hierarchical factorization of the conditional class distribution.
PNM: Pixel Null Model for General Image Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

A major challenge in image segmentation is classifying object boundaries. Recent efforts propose to refine the segmentation result with boundary masks. However, models are still prone to misclassifying boundary pixels even when they correctly capture the object contours. In such cases, even a perfect boundary map is unhelpful for segmentation refinement. In this paper, we argue that assigning proper prior weights to error-prone pixels such as object boundaries can significantly improve the segmentation quality. Specifically, we present the \textit{pixel null model} (PNM), a prior model that weights each pixel according to its probability of being correctly classified by a random segmenter. Empirical analysis shows that PNM captures the misclassification distribution of different state-of-the-art (SOTA) segmenters. Extensive experiments on semantic, instance, and panoptic segmentation tasks over three datasets (Cityscapes, ADE20K, MS COCO) confirm that PNM consistently improves the segmentation quality of most SOTA methods (including the vision transformers) and outperforms boundary-based methods by a large margin. We also observe that the widely-used mean IoU (mIoU) metric is insensitive to boundaries of different sharpness. As a byproduct, we propose a new metric, \textit{PNM IoU}, which perceives the boundary sharpness and better reflects the model segmentation performance in error-prone regions.
Towards Semantic Communications: A Paradigm Shift
Venue: arXiv cs.IT 2022
Year: 2022

The last seventy years have witnessed the transition of communication from Shannon's theoretical concept to current high-efficient practical systems. Classical communication systems address the capability-deficiency issue mainly by module-stacking and technique-densification with ever-increasing complexity. In such a traditional viewpoint, classical source coding only uses explicit probabilistic models to compress data, regardless of the meaning of transmitted source messages. Also, channel coded transmission does not identify the source content. In this sense, state-of-the-art communication systems work merely at the technical level as summarized by Weaver. Unlike the traditional system design philosophy, this article proposes a new route to boost the system capabilities towards intelligence-endogenous and primitive-concise communications. The communication paradigm upgrades to the semantic level, which is radically different since all the key techniques imply the use of meanings of transmitted data, thus deeply changing the design of the communication system. This paradigm shifting unveils a promising direction due to its ability to offer an identical quality of service with much lower data transmission requirement. Different from other similar works, this article constitutes a brief tutorial on the framework of semantic communications, its gain analyzed from the information theory perspective, a method to calculate the semantic compression bound, and an exemplary use case of semantic communications.
First Experiences in Performance Benchmarking with the New SPEChpc 2021 Suites
Venue: arXiv cs.DC 2022
Year: 2022

Modern HPC systems are built with innovative system architectures and novel programming models to further push the speed limit of computing. The increased complexity poses challenges for performance portability and performance evaluation. The Standard Performance Evaluation Corporation -SPEC has a long history of producing industry standard benchmarks for modern computer systems. SPEC is a newly released SPEChpc 2021 benchmark suites, developed by the High Performance Group, are a bold attempt to provide a fair and objective benchmarking tool designed for state of the art HPC systems. With the support of multiple host and accelerator programming models, the suites are portable across both homogeneous and heterogeneous architectures. Different workloads are developed to fit system sizes ranging from a few compute nodes to a few hundred compute nodes. In this manuscript, we take a first glance at these benchmark suites and evaluate their portability and basic performance characteristics on various popular and emerging HPC architectures, including x86 CPU, NVIDIA GPU, and AMD GPU. This study provides a first-hand experience of executing the SPEChpc 2021 suites at scale on production HPC systems, discusses real-world use cases, and serves as an initial guideline for using the benchmark suites.
Euclidean Invariant Recognition of 2D Shapes Using Histograms of Magnitudes of Local Fourier-Mellin Descriptors
Venue: arXiv cs.CV 2022
Year: 2022

Because the magnitude of inner products with its basis functions are invariant to rotation and scale change, the Fourier-Mellin transform has long been used as a component in Euclidean invariant 2D shape recognition systems. Yet Fourier-Mellin transform magnitudes are only invariant to rotation and scale changes about a known center point, and full Euclidean invariant shape recognition is not possible except when this center point can be consistently and accurately identified. In this paper, we describe a system where a Fourier-Mellin transform is computed at every point in the image. The spatial support of the Fourier-Mellin basis functions is made local by multiplying them with a polynomial envelope. Significantly, the magnitudes of convolutions with these complex filters at isolated points are not (by themselves) used as features for Euclidean invariant shape recognition because reliable discrimination would require filters with spatial support large enough to fully encompass the shapes. Instead, we rely on the fact that normalized histograms of magnitudes are fully Euclidean invariant. We demonstrate a system based on the VLAD machine learning method that performs Euclidean invariant recognition of 2D shapes and requires an order of magnitude less training data than comparable methods based on convolutional neural networks.
DKMA-ULD: Domain Knowledge augmented Multi-head Attention based Robust Universal Lesion Detection
Venue: arXiv cs.CV 2022
Year: 2022

Incorporating data-specific domain knowledge in deep networks explicitly can provide important cues beneficial for lesion detection and can mitigate the need for diverse heterogeneous datasets for learning robust detectors. In this paper, we exploit the domain information present in computed tomography (CT) scans and propose a robust universal lesion detection (ULD) network that can detect lesions across all organs of the body by training on a single dataset, DeepLesion. We analyze CT-slices of varying intensities, generated using heuristically determined Hounsfield Unit(HU) windows that individually highlight different organs and are given as inputs to the deep network. The features obtained from the multiple intensity images are fused using a novel convolution augmented multi-head self-attention module and subsequently, passed to a Region Proposal Network (RPN) for lesion detection. In addition, we observed that traditional anchor boxes used in RPN for natural images are not suitable for lesion sizes often found in medical images. Therefore, we propose to use lesion-specific anchor sizes and ratios in the RPN for improving the detection performance. We use self-supervision to initialize weights of our network on the DeepLesion dataset to further imbibe domain knowledge. Our proposed Domain Knowledge augmented Multi-head Attention based Universal Lesion Detection Network DMKA-ULD produces refined and precise bounding boxes around lesions across different organs. We evaluate the efficacy of our network on the publicly available DeepLesion dataset which comprises of approximately 32K CT scans with annotated lesions across all organs of the body. Results demonstrate that we outperform existing state-of-the-art methods achieving an overall sensitivity of 87.16%.
Low c-Differential Uniformity of the Swapped Inverse Function in Odd Characteristic
Venue: arXiv cs.IT 2022
Year: 2022

The study of Boolean functions with low $c$-differential uniformity has become recently an important topic of research. However, in odd characteristic case, there are not many results on the ($c$-)differential uniformity of functions that are not power functions. In this paper, we investigate the $c$-differential uniformity of the swapped inverse functions in odd characteristic, and show that their $c$-differential uniformities are at most 6 except for some special case.
S$^2$SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers
Venue: arXiv cs.CL 2022
Year: 2022

The task of converting a natural language question into an executable SQL query, known as text-to-SQL, is an important branch of semantic parsing. The state-of-the-art graph-based encoder has been successfully used in this task but does not model the question syntax well. In this paper, we propose S$^2$SQL, injecting Syntax to question-Schema graph encoder for Text-to-SQL parsers, which effectively leverages the syntactic dependency information of questions in text-to-SQL to improve the performance. We also employ the decoupling constraint to induce diverse relational edge embedding, which further improves the network's performance. Experiments on the Spider and robustness setting Spider-Syn demonstrate that the proposed approach outperforms all existing methods when pre-training models are used, resulting in a performance ranks first on the Spider leaderboard.
Playing (Almost-)Optimally in Concurrent Büchi and co-Büchi Games
Venue: arXiv cs.GT 2022
Year: 2022

We study two-player concurrent stochastic games on finite graphs, with Büchi and co-Büchi objectives. The goal of the first player is to maximize the probability of satisfying the given objective. Following Martin's determinacy theorem for Blackwell games, we know that such games have a value. Natural questions are then: does there exist an optimal strategy, that is, a strategy achieving the value of the game? what is the memory required for playing (almost-)optimally? The situation is rather simple to describe for turn-based games, where positional pure strategies suffice to play optimally in games with parity objectives. Concurrency makes the situation intricate and heterogeneous. For most ω-regular objectives, there do indeed not exist optimal strategies in general. For some objectives (that we will mention), infinite memory might also be required for playing optimally or almost-optimally. We also provide characterizations of local interactions of the players to ensure positionality of (almost-)optimal strategies for Büchi and co-Büchi objectives. This characterization relies on properties of game forms underpinning the formalism for defining local interactions of the two players. These well-behaved game forms are like elementary bricks which, when they behave well in isolation, can be assembled in graph games and ensure the good property for the whole game.
SMC4PEP: Stochastic Model Checking of Product Engineering Processes
Venue: arXiv cs.LO 2022
Year: 2022

Product Engineering Processes (PEPs) are used for describing complex product developments in big enterprises such as automotive and avionics industries. The Business Process Model Notation (BPMN) is a widely used language to encode interactions among several participants in such PEPs. In this paper, we present SMC4PEP as a tool to convert graphical representations of a business process using the BPMN standard to an equivalent discrete-time stochastic control process called Markov Decision Process (MDP). To this aim, we first follow the approach described in an earlier investigation to generate a semantically equivalent business process which is more capable of handling the PEP complexity. In particular, the interaction between different levels of abstraction is realized by events rather than direct message flows. Afterwards, SMC4PEP converts the generated process to an MDP model described by the syntax of the probabilistic model checking tool PRISM. As such, SMC4PEP provides a framework for automatic verification and validation of business processes in particular with respect to requirements from legal standards such as Automotive SPICE. Moreover, our experimental results confirm a faster verification routine due to smaller MDP models generated from the alternative event-based BPMN models.
COP: Control & Observability-aware Planning
Venue: arXiv cs.RO 2022
Year: 2022

In this research, we aim to answer the question: How to combine Closed-Loop State and Input Sensitivity-based with Observability-aware trajectory planning? These possibly opposite optimization objectives can be used to improve trajectory control tracking and, at the same time, estimation performance. Our proposed novel Control & Observability-aware Planning (COP) framework is the first that uses these possibly opposing objectives in a Single-Objective Optimization Problem (SOOP) based on the Augmented Weighted Tchebycheff method to perform the balancing of them and generation of Bézier curve-based trajectories. Statistically relevant simulations for a 3D quadrotor unmanned aerial vehicle (UAV) case study produce results that support our claims and show the negative correlation between both objectives. We were able to reduce the positional mean integral error norm as well as the estimation uncertainty with the same trajectory to comparable levels of the trajectories optimized with individual objectives.
The many facets of academic mobility and its impact on scholars' career
Venue: arXiv cs.DL 2022
Year: 2022

International mobility in academia can enhance the human and social capital of researchers and consequently their scientific outcome. However, there is still a very limited understanding of the different mobility patterns among scholars with various socio-demographic characteristics. The aim of this study is twofold. First, we investigate to what extent individual factors associate with the mobility of researchers. Second, we explore the relationship between mobility and scientific activity and impact. For this purpose, we used a bibliometric approach to track the mobility of authors. To compare the scientific outcomes of researchers, we considered the number of publications and received citations as indicators, as well as the number of unique co-authors in all their publications. We also analysed the co-authorship network of researchers and compared centrality measures of mobile and non-mobile researchers. Results show that researchers from North America and Sub-Saharan Africa, particularly female ones, have the lowest, respectively, highest tendency towards international mobility. Having international co-authors increases the probability of international movement. Our findings uncover gender inequality in international mobility across scientific fields and countries. Across genders, researchers in the Physical sciences have the most and in the Social sciences the least rate of mobility. We observed more mobility for Social scientists at the advanced career stage, while researchers in other fields prefer to move at earlier career stages. Also, we found a positive correlation between mobility and scientific outcomes, but no apparent difference between females and males. Comparing the centrality of mobile and non-mobile researchers in the co-authorship networks reveals a higher social capital advantage for mobile researchers.
Trustable Mobile Crowd Sourcing for Acquiring Information from a Flooded Smart Area
Venue: arXiv cs.CY 2022
Year: 2022

Flood is a natural phenomenon that causes severe environmental damage and destruction in smart cities. After a flood, topographic, geological, and living conditions change. As a result, the previous information regarding the environment is no more valid. Rescue and relief organizations that intend to help the affected people need to obtain new and accurate information about the conditions of the flooded environment. Acquiring this required information in the shortest time is a challenge for realizing smart cities. Due to the advances in the Internet of Things technology and the prevalence of smartphones with several sensors and functionalities, it is possible to obtain the required information by leveraging the Crowdsourcing model. In this paper, the information required from a flooded area is classified into four categories: victim, Facility and Livelihood, medical, and transfer. Next, a crowdsourcing scheme for acquiring information is proposed, including malicious user detection to ensure the accuracy of information received. Finally, simulation results indicate that the proposed scheme correctly detects malicious users and ensures the quality of obtained information.
Model Positionality and Computational Reflexivity: Promoting Reflexivity in Data Science
Venue: arXiv cs.CY 2022
Year: 2022

Data science and machine learning provide indispensable techniques for understanding phenomena at scale, but the discretionary choices made when doing this work are often not recognized. Drawing from qualitative research practices, we describe how the concepts of positionality and reflexivity can be adapted to provide a framework for understanding, discussing, and disclosing the discretionary choices and subjectivity inherent to data science work. We first introduce the concepts of model positionality and computational reflexivity that can help data scientists to reflect on and communicate the social and cultural context of a model's development and use, the data annotators and their annotations, and the data scientists themselves. We then describe the unique challenges of adapting these concepts for data science work and offer annotator fingerprinting and position mining as promising solutions. Finally, we demonstrate these techniques in a case study of the development of classifiers for toxic commenting in online communities.
Ensemble plasticity and network adaptability in SNNs
Venue: arXiv cs.LG 2022
Year: 2022

Artificial Spiking Neural Networks (ASNNs) promise greater information processing efficiency because of discrete event-based (i.e., spike) computation. Several Machine Learning (ML) applications use biologically inspired plasticity mechanisms as unsupervised learning techniques to increase the robustness of ASNNs while preserving efficiency. Spike Time Dependent Plasticity (STDP) and Intrinsic Plasticity (IP) (i.e., dynamic spiking threshold adaptation) are two such mechanisms that have been combined to form an ensemble learning method. However, it is not clear how this ensemble learning should be regulated based on spiking activity. Moreover, previous studies have attempted threshold based synaptic pruning following STDP, to increase inference efficiency at the cost of performance in ASNNs. However, this type of structural adaptation, that employs individual weight mechanisms, does not consider spiking activity for pruning which is a better representation of input stimuli. We envisaged that plasticity-based spike-regulation and spike-based pruning will result in ASSNs that perform better in low resource situations. In this paper, a novel ensemble learning method based on entropy and network activation is introduced, which is amalgamated with a spike-rate neuron pruning technique, operated exclusively using spiking activity. Two electroencephalography (EEG) datasets are used as the input for classification experiments with a three-layer feed forward ASNN trained using one-pass learning. During the learning process, we observed neurons assembling into a hierarchy of clusters based on spiking rate. It was discovered that pruning lower spike-rate neuron clusters resulted in increased generalization or a predictable decline in performance.